PT  - JOURNAL ARTICLE
AU  - Kaushik J Lakshminarasimhan
AU  - Alexandre Pouget
AU  - Gregory C DeAngelis
AU  - Dora E Angelaki
AU  - Xaq Pitkow
TI  - Inferring decoding strategies for multiple correlated neural populations
AID  - 10.1101/108019
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 108019
4099  - http://biorxiv.org/content/early/2017/05/22/108019.short
4100  - http://biorxiv.org/content/early/2017/05/22/108019.full
AB  - Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.Author Summary The neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animal’s percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models can be distinguished by an experiment that measures correlations while inactivating different brain areas.